title: Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks.
abstract: Diel dissolved oxygen (DO) time series measured continuously using proximal sensors in situ for a temperate lake were denoised using discrete wavelet transform (DWT) with the orthogonal wavelet families of coiflet, daubechies, and symmlet with order of 10. Diel DO time series denoised were modeled using nine temporal artificial neural networks (ANNs) as a function of water level, water temperature, electrical conductivity, pH, day of year, and hour. Our results showed that time-lag recurrent network (TLRN) using denoised data emulated diel DO dynamics better than the best-performing TLRN using the original data, time-delay neural network (TDNN), and recurrent network (RNN). Daubechies basis dealt with diel DO data slightly better than the other bases given its coefficient of determination (r (2) = 87.1 %), while symmlet performed slightly better than the other bases in terms of root mean square error (RMSE = 1.2 ppm) and mean absolute error (MAE = 0.9 ppm).
authors.affiliation: Department of Environmental Engineering, Abant Izzet Baysal University, Bolu, Turkey, fevrendilek@ibu.edu.tr.
authors.forename: Fatih
authors.initials: F
authors.lastname: Evrendilek
authors.forename: Nusret
authors.initials: N
authors.lastname: Karakaya
mesh_terms: D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods*; D060106:Lakes / Q000737:chemistry; D016571:Neural Networks, Computer; D010100:Oxygen / Q000032:analysis* / Q000737:chemistry; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data
chemical_list: D014874:Water Pollutants, Chemical; D010100:Oxygen